docstr: This interface infers the topic distribution of document.
  
  Args:
      document(str): the input document text.
  
  Returns:
      results(list): returns the topic distribution of document.
docstr_tok: ["This","interface","infers","the","topic","distribution","of","document",".","Args",":","document","(","str",")",":","the","input","document","text",".","Returns",":","results","(","list",")",":","returns","the","topic","distribution","of","document","."]
code: def infer_doc_topic_distribution(self, document):
      """
      This interface infers the topic distribution of document.
  
      Args:
          document(str): the input document text.
  
      Returns:
          results(list): returns the topic distribution of document.
      """
      tokens = self.__tokenizer.tokenize(document)
      if tokens == []:
          return []
      results = []
      doc = LDADoc()
      self.__engine.infer(tokens, doc)
      topics = doc.sparse_topic_dist()
      for topic in topics:
          results.append(
              {"topic id": topic.tid, "distribution": topic.prob}
          )
      return results
  
file_path: PaddleHub/modules/text/language_model/lda_novel/module.py
imports: import os
  import paddlehub
  from paddlehub.module.module import moduleinfo
  from paddlehub.common.logger import logger
  from lda_novel.inference_engine import InferenceEngine
  from lda_novel.document import LDADoc, SLDADoc
  from lda_novel.semantic_matching import SemanticMatching, WordAndDis
  from lda_novel.tokenizer import LACTokenizer, SimpleTokenizer
  from lda_novel.config import ModelType
  from lda_novel.vocab import Vocab, WordCount
label_desc: Use image node_id 5 for calling the TopicModel obj's underlying member method code with example usage: obj.infer_doc_topic_distribution(document) and returns: results, list